1d
A machine learning model may enable liver cancer risk prediction with routine clinical information
A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results ...
A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results predicted a patient's risk of hepatocellular carcinoma (HCC), the most common ...
Long-term lithium therapy remains the most effective maintenance treatment for bipolar disorder, yet it poses a significant ...
Objective To estimate the prevalence of potential overtreatment of type 2 diabetes mellitus (T2DM) among older adults and to develop and compare predictive models to identify patient and physician ...
Using routine clinical data, the model gauges liver cancer risk better than existing tools, offering a potential way to identify high-risk patients missed by current screening criteria.
The frequency of substance use, early age of initiation, and cannabis-related memory impairments are among the primary ...
Predictive risk scores created using administrative claims and publicly available social determinants of health data strongly ...
Ophthalmology pioneered the clinical use of artificial intelligence (AI). Beyond automating routine tasks, including retinal ...
Detecting behavioural signatures of depression from everyday digital traces is a central challenge in computational ...
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